import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase__  =					logging.get_logger(__name__)


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    UniSpeechSatForSequenceClassification.from_pretrained(a__    ,     config=a__ )
  _UpperCamelCase           =    downstream_dict["projector.weight"]
  _UpperCamelCase           =    downstream_dict["projector.bias"]
  _UpperCamelCase           =    downstream_dict["model.post_net.linear.weight"]
  _UpperCamelCase           =    downstream_dict["model.post_net.linear.bias"]
  return model


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

  _UpperCamelCase           =    UniSpeechSatForAudioFrameClassification.from_pretrained(a__    ,     config=a__ )
  _UpperCamelCase           =    downstream_dict["model.linear.weight"]
  _UpperCamelCase           =    downstream_dict["model.linear.bias"]
  return model


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    UniSpeechSatForXVector.from_pretrained(a__    ,     config=a__ )
  _UpperCamelCase           =    downstream_dict["connector.weight"]
  _UpperCamelCase           =    downstream_dict["connector.bias"]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
    _UpperCamelCase           =    downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    _UpperCamelCase           =    downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  _UpperCamelCase           =    downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
  _UpperCamelCase           =    downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
  _UpperCamelCase           =    downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
  _UpperCamelCase           =    downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
  _UpperCamelCase           =    downstream_dict["objective.W"]
  return model


@torch.no_grad()
def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

  _UpperCamelCase           =    torch.load(a__    ,     map_location="cpu" )

  _UpperCamelCase           =    checkpoint["Downstream"]

  _UpperCamelCase           =    UniSpeechSatConfig.from_pretrained(a__ )
  _UpperCamelCase           =    WavaVecaFeatureExtractor.from_pretrained(
      a__    ,     return_attention_mask=a__    ,     do_normalize=a__ )

  _UpperCamelCase           =    hf_config.architectures[0]
  if arch.endswith("ForSequenceClassification" ):
    _UpperCamelCase           =    convert_classification(a__    ,     a__    ,     a__ )
  elif arch.endswith("ForAudioFrameClassification" ):
    _UpperCamelCase           =    convert_diarization(a__    ,     a__    ,     a__ )
  elif arch.endswith("ForXVector" ):
    _UpperCamelCase           =    convert_xvector(a__    ,     a__    ,     a__ )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )

  if hf_config.use_weighted_layer_sum:
    _UpperCamelCase           =    checkpoint["Featurizer"]["weights"]

  hf_feature_extractor.save_pretrained(a__ )
  hf_model.save_pretrained(a__ )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()
   parser.add_argument(
       '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
   )
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
   parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
   lowerCamelCase__  =					parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )




